Build a 30-Day Readmission Risk Model on De-Identified EHR Data
Overview
What this challenge is about.
Train a gradient-boosting and Transformer model on de-identified EHR data, audit fairness gaps, and deliver a clinical memo. Earn a verifiable certificate.
The scenario
The startup (around 35 staff, Series A, partnered with two US health plans) treats every readmission model as a clinically-reviewed deliverable and writes a model card for each.
The Brief
What you'll do, and what you'll demonstrate.
Build and audit a 30-day readmission risk model on de-identified EHR data with calibration and fairness reported alongside discrimination.
Earning criteria — what you'll demonstrate
- Apply ML to a real EHR-derived clinical-risk prediction problem
- Calibrate clinical-grade classifiers and report ECE alongside AUROC
- Audit subgroup performance gaps and reason about clinical equity
- Produce a model card that survives clinical review
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Machine Learning for Healthcare and Biomedicine
Master · Applied Ai
Strong alignment
This challenge maps to Machine Learning for Healthcare and Biomedicine at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Ehr Modeling
Apply ehr modeling to solve real industry problems and demonstrate production-level capability.
- Risk Stratification
Apply risk stratification to solve real industry problems and demonstrate production-level capability.
- Model Calibration
Apply model calibration to solve real industry problems and demonstrate production-level capability.
- Fairness Metrics
Apply fairness metrics to solve real industry problems and demonstrate production-level capability.
- Transformer
Apply transformer to solve real industry problems and demonstrate production-level capability.
- Gradient Boosting
Apply gradient boosting to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Applied AI Scientist
Clinical-grade risk models with calibration + fairness audits are the applied-AI-scientist's signature work at any payer-facing healthtech startup.
This challenge sharpens
- risk-stratification
- model-calibration
- fairness-metrics
ML Researcher
Comparing a tree ensemble to a sequence model on EHR data with rigorous reporting is the kind of focused study clinical-ML hiring loops grade.
This challenge sharpens
- ehr-modeling
- transformer
- gradient-boosting
AI Safety Researcher
Subgroup fairness audits and model cards on clinical models are exactly the AI-safety-researcher's contribution to any healthtech product.
This challenge sharpens
- fairness-metrics
- model-calibration
- risk-stratification